Pub Date : 2026-04-01Epub Date: 2026-02-06DOI: 10.1016/j.sbi.2025.103218
Ashar J. Malik , Stephanie Portelli , David B. Ascher
Transformers are rapidly reshaping structural biology. We argue the reason is “Emergent Latent Biology” (ELB): transformers place proteins into high-dimensional representations where hidden biophysical patterns become easier to see. We explore this concept across four key areas: protein folding, variant effects, protein–protein and protein–drug interactions. Highlighting recent gains, we note that traditional, physics-based calculations are still required for the hardest quantitative jobs, like predicting precise binding strength. Furthermore, we draw attention to major pitfalls, arguing progress depends on solving the critical “chemistry gap,” modelling chemical modifications, and the “dynamics gap”, predicting protein movement, which requires better validation methods and new large-scale experiments.
{"title":"Transformers as a substrate for structural biology","authors":"Ashar J. Malik , Stephanie Portelli , David B. Ascher","doi":"10.1016/j.sbi.2025.103218","DOIUrl":"10.1016/j.sbi.2025.103218","url":null,"abstract":"<div><div>Transformers are rapidly reshaping structural biology. We argue the reason is “Emergent Latent Biology” (ELB): transformers place proteins into high-dimensional representations where hidden biophysical patterns become easier to see. We explore this concept across four key areas: protein folding, variant effects, protein–protein and protein–drug interactions. Highlighting recent gains, we note that traditional, physics-based calculations are still required for the hardest quantitative jobs, like predicting precise binding strength. Furthermore, we draw attention to major pitfalls, arguing progress depends on solving the critical “chemistry gap,” modelling chemical modifications, and the “dynamics gap”, predicting protein movement, which requires better validation methods and new large-scale experiments.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"97 ","pages":"Article 103218"},"PeriodicalIF":6.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146137351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-04-01Epub Date: 2026-03-02DOI: 10.1016/j.sbi.2026.103236
Thomas Löhr , Gogulan Karunanithy , Gabriella T. Heller
Intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) are critical regulators in health and disease but remain underexploited as drug targets. Unlike folded proteins, they populate dynamic ensembles where interactions can be transient or multivalent, and both enthalpic and entropic contributions shape binding, complicating ligand discovery. Here, we analyze three key barriers hindering progress: (1) nontraditional binding mechanisms that challenge classical drug design, (2) experimental and computational limitations for studying disorder, and (3) a lack of systematic datasets. Our analysis of the Biological Magnetic Resonance Data Bank (BMRB) and BindingDB highlights the extreme underrepresentation of IDPs and IDRs, underscoring the need for community-driven data resources. By integrating new binding paradigms, tailored methodologies, and standardized datasets, drug discovery can begin to harness IDPs as a new therapeutic frontier.
{"title":"Why are there no clinically-approved drugs targeting disordered proteins?","authors":"Thomas Löhr , Gogulan Karunanithy , Gabriella T. Heller","doi":"10.1016/j.sbi.2026.103236","DOIUrl":"10.1016/j.sbi.2026.103236","url":null,"abstract":"<div><div>Intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) are critical regulators in health and disease but remain underexploited as drug targets. Unlike folded proteins, they populate dynamic ensembles where interactions can be transient or multivalent, and both enthalpic and entropic contributions shape binding, complicating ligand discovery. Here, we analyze three key barriers hindering progress: (1) nontraditional binding mechanisms that challenge classical drug design, (2) experimental and computational limitations for studying disorder, and (3) a lack of systematic datasets. Our analysis of the Biological Magnetic Resonance Data Bank (BMRB) and BindingDB highlights the extreme underrepresentation of IDPs and IDRs, underscoring the need for community-driven data resources. By integrating new binding paradigms, tailored methodologies, and standardized datasets, drug discovery can begin to harness IDPs as a new therapeutic frontier.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"97 ","pages":"Article 103236"},"PeriodicalIF":6.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147347623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-04-01Epub Date: 2026-02-12DOI: 10.1016/j.sbi.2026.103222
Shan Sun, Sen-Fang Sui
Membrane protein complexes are essential for cellular functions, which rely on both constituent protein structures and their interactions within native membranes. While in vitro methods have successfully yielded high-resolution structures of individual proteins and subcomplexes, these approaches typically require detergent extraction and extensive purification, which can disrupt the native membrane environment and potentially alter the supramolecular organization. In situ structural biology has therefore emerged as an effective strategy to overcome these limitations by directly visualizing macromolecular machines within their physiological context. With continuous technological advancements, several recent studies have resolved in situ structures of large protein complexes at high or even near-atomic resolution. This review focuses on recent in situ high-resolution studies of membrane protein megacomplexes, highlighting key technical innovations, structural insights, and the remaining challenges and opportunities in the field.
{"title":"In situ structural studies of membrane protein megacomplexes","authors":"Shan Sun, Sen-Fang Sui","doi":"10.1016/j.sbi.2026.103222","DOIUrl":"10.1016/j.sbi.2026.103222","url":null,"abstract":"<div><div>Membrane protein complexes are essential for cellular functions, which rely on both constituent protein structures and their interactions within native membranes. While <em>in vitro</em> methods have successfully yielded high-resolution structures of individual proteins and subcomplexes, these approaches typically require detergent extraction and extensive purification, which can disrupt the native membrane environment and potentially alter the supramolecular organization. <em>In situ</em> structural biology has therefore emerged as an effective strategy to overcome these limitations by directly visualizing macromolecular machines within their physiological context. With continuous technological advancements, several recent studies have resolved <em>in situ</em> structures of large protein complexes at high or even near-atomic resolution. This review focuses on recent <em>in situ</em> high-resolution studies of membrane protein megacomplexes, highlighting key technical innovations, structural insights, and the remaining challenges and opportunities in the field.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"97 ","pages":"Article 103222"},"PeriodicalIF":6.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146171553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-04-01Epub Date: 2026-02-12DOI: 10.1016/j.sbi.2026.103224
Joel J. Chubb , Aimee L. Boyle , Katherine I. Albanese
Protein design enables the creation of novel structures and functions beyond those found in nature, with recent progress accelerated by computational modeling and machine learning. However, many automated methods act as black boxes, limiting mechanistic insight. Here we highlight the continuing importance of rational protein design, defined as an approach rooted in physical principles, chemical intuition, and sequence–structure–function relationships. We outline three complementary strategies: backbone-first, sequence-first, and function-first, which provide interpretable design frameworks and enable robust scaffold generation, motif incorporation, and functional engineering. Looking forward, we argue that hybrid workflows combining rational principles with machine learning offer the most promising route to dynamic, explainable, and generalizable protein design.
{"title":"Rational protein design","authors":"Joel J. Chubb , Aimee L. Boyle , Katherine I. Albanese","doi":"10.1016/j.sbi.2026.103224","DOIUrl":"10.1016/j.sbi.2026.103224","url":null,"abstract":"<div><div>Protein design enables the creation of novel structures and functions beyond those found in nature, with recent progress accelerated by computational modeling and machine learning. However, many automated methods act as black boxes, limiting mechanistic insight. Here we highlight the continuing importance of rational protein design, defined as an approach rooted in physical principles, chemical intuition, and sequence–structure–function relationships. We outline three complementary strategies: backbone-first, sequence-first, and function-first, which provide interpretable design frameworks and enable robust scaffold generation, motif incorporation, and functional engineering. Looking forward, we argue that hybrid workflows combining rational principles with machine learning offer the most promising route to dynamic, explainable, and generalizable protein design.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"97 ","pages":"Article 103224"},"PeriodicalIF":6.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146171552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-04-01Epub Date: 2026-02-18DOI: 10.1016/j.sbi.2026.103225
Matteo Cagiada , Charlotte M. Deane
{"title":"Moving the antibody: Molecular dynamics for molecular mechanisms and developability","authors":"Matteo Cagiada , Charlotte M. Deane","doi":"10.1016/j.sbi.2026.103225","DOIUrl":"10.1016/j.sbi.2026.103225","url":null,"abstract":"","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"97 ","pages":"Article 103225"},"PeriodicalIF":6.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146225816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-04-01Epub Date: 2026-02-07DOI: 10.1016/j.sbi.2025.103214
Antonio J. Ortiz , Antoniel A.S. Gomes , Pedro Renault , David Romero , Antoni Guillamon , Jesús Giraldo
Drug–target residence time (τ) is reviewed from two perspectives: mathematics and molecular dynamics. The first focuses on the quantification of τ using a mathematical formalism applicable to different pharmacological mechanistic conditions. This formalism is based on the concept of the smallest-modulus eigenvalue of a subsystem of interest, in which the global formation process has been eliminated. The second includes relevant studies of recent years to provide a structural explanation of τ predictions. Special attention is paid to physically supported artificial intelligence methods. The main objective of this minireview is to promote a combined approach in which mathematics and physics work synergistically to describe the complexity associated with τ in G protein-coupled receptors.
{"title":"Drug–target residence time: Analyzing cooperativity effects in G protein-coupled receptors by mathematical modeling and molecular dynamics simulations","authors":"Antonio J. Ortiz , Antoniel A.S. Gomes , Pedro Renault , David Romero , Antoni Guillamon , Jesús Giraldo","doi":"10.1016/j.sbi.2025.103214","DOIUrl":"10.1016/j.sbi.2025.103214","url":null,"abstract":"<div><div>Drug–target residence time (τ) is reviewed from two perspectives: mathematics and molecular dynamics. The first focuses on the quantification of τ using a mathematical formalism applicable to different pharmacological mechanistic conditions. This formalism is based on the concept of the smallest-modulus eigenvalue of a subsystem of interest, in which the global formation process has been eliminated. The second includes relevant studies of recent years to provide a structural explanation of τ predictions. Special attention is paid to physically supported artificial intelligence methods. The main objective of this minireview is to promote a combined approach in which mathematics and physics work synergistically to describe the complexity associated with τ in G protein-coupled receptors.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"97 ","pages":"Article 103214"},"PeriodicalIF":6.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146141361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-04-01Epub Date: 2026-01-30DOI: 10.1016/j.sbi.2025.103217
Riccardo Solazzo , Shu-Yu Chen , Sereina Riniker
Proteolysis-targeting chimeras (PROTACs) and molecular glues promote targeted protein degradation by recruiting an E3 ligase to proteins of interest (POIs). An accurate 3D structure of the ternary complex formed by E3 ligase, ligand, and POI is central to the rational design of degraders. Elucidating this structure with crystallography or cryo-EM can be challenging due to conformational flexibility, dynamic protein-protein interactions, and high-dimensional binding landscapes. To facilitate structure-based design in the absence of an experimental structure, computational approaches have been proposed: (i) multistep methods involving traditional docking pipelines, and (ii) single-step methods with deep learning models to directly predict the complex structure. Multistep methods are limited by sampling complexity, accurate input structures, scoring accuracy, and computational cost, while single-step methods are faster but are constrained by training-data scarcity. Here, we examine recent advances and emerging tools in modeling ternary complexes, critically discuss their predictive power and limitations, and highlight remaining challenges.
{"title":"Machine learning, docking, or physics for structure prediction of ligand-induced ternary complexes","authors":"Riccardo Solazzo , Shu-Yu Chen , Sereina Riniker","doi":"10.1016/j.sbi.2025.103217","DOIUrl":"10.1016/j.sbi.2025.103217","url":null,"abstract":"<div><div>Proteolysis-targeting chimeras (PROTACs) and molecular glues promote targeted protein degradation by recruiting an E3 ligase to proteins of interest (POIs). An accurate 3D structure of the ternary complex formed by E3 ligase, ligand, and POI is central to the rational design of degraders. Elucidating this structure with crystallography or cryo-EM can be challenging due to conformational flexibility, dynamic protein-protein interactions, and high-dimensional binding landscapes. To facilitate structure-based design in the absence of an experimental structure, computational approaches have been proposed: (i) multistep methods involving traditional docking pipelines, and (ii) single-step methods with deep learning models to directly predict the complex structure. Multistep methods are limited by sampling complexity, accurate input structures, scoring accuracy, and computational cost, while single-step methods are faster but are constrained by training-data scarcity. Here, we examine recent advances and emerging tools in modeling ternary complexes, critically discuss their predictive power and limitations, and highlight remaining challenges.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"97 ","pages":"Article 103217"},"PeriodicalIF":6.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-04-01Epub Date: 2026-02-05DOI: 10.1016/j.sbi.2025.103216
Utkarsh Upadhyay, Anton Dorn, Christian Faber, Alexander Schug
RNA structure prediction remains one of the most challenging problems in computational biology, with significant implications for understanding gene regulation, drug design, and synthetic biology. While deep learning has revolutionized protein structure prediction, RNA presents unique challenges including limited training data, complex noncanonical interactions, and conformational flexibility. This review examines the evolution from traditional physics-based methods to current deep learning approaches for RNA secondary and tertiary structure prediction. After briefly exploring traditional methods, like Direct Coupling Analysis and physics-based simulations, we systematically review three deep learning paradigms: language model–based methods, end-to-end structure predictors, and geometry-distance prediction approaches. Furthermore, we identify critical future research directions focusing on advanced tokenization strategies to address data scarcity and explainable artificial intelligence techniques to improve model interpretability. Despite significant progress, achieving transformative performance requires continued methodological innovation, specifically designed for RNA’s unique characteristics, and a substantial expansion of high-quality structural datasets.
{"title":"From sequence to structure: A comprehensive review of deep learning models for RNA structure prediction","authors":"Utkarsh Upadhyay, Anton Dorn, Christian Faber, Alexander Schug","doi":"10.1016/j.sbi.2025.103216","DOIUrl":"10.1016/j.sbi.2025.103216","url":null,"abstract":"<div><div>RNA structure prediction remains one of the most challenging problems in computational biology, with significant implications for understanding gene regulation, drug design, and synthetic biology. While deep learning has revolutionized protein structure prediction, RNA presents unique challenges including limited training data, complex noncanonical interactions, and conformational flexibility. This review examines the evolution from traditional physics-based methods to current deep learning approaches for RNA secondary and tertiary structure prediction. After briefly exploring traditional methods, like Direct Coupling Analysis and physics-based simulations, we systematically review three deep learning paradigms: language model–based methods, end-to-end structure predictors, and geometry-distance prediction approaches. Furthermore, we identify critical future research directions focusing on advanced tokenization strategies to address data scarcity and explainable artificial intelligence techniques to improve model interpretability. Despite significant progress, achieving transformative performance requires continued methodological innovation, specifically designed for RNA’s unique characteristics, and a substantial expansion of high-quality structural datasets.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"97 ","pages":"Article 103216"},"PeriodicalIF":6.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146131433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-04-01Epub Date: 2026-02-19DOI: 10.1016/j.sbi.2026.103226
George Hedger , Edward Lyman , Sarah L. Rouse
Membrane lipids can bind to specific sites on membrane proteins in a ligand-like manner and modulate protein structure and function. Molecular dynamics simulations encompass a suite of approaches to identify, characterise, and explain the atomic-level mechanisms that underlie the functional effects of ligand-like lipids on membrane proteins. Simulations have shown good agreement with available structural data on lipid-protein interactions. Building on successes, simulations are now used to identify new interactions and mechanisms de novo for a given membrane protein. In this age of abundance, it is increasingly possible to analyse patterns across large groups of proteins and in ever more complex membrane environments. The dawn of machine learning approaches in lipid-protein cofolding holds considerable promise to synergistically capitalise on this availability of simulation data and uncover new facets of ligand-like lipid biology.
{"title":"Ligand-like lipid interactions with membrane proteins: Simulations and machine learning","authors":"George Hedger , Edward Lyman , Sarah L. Rouse","doi":"10.1016/j.sbi.2026.103226","DOIUrl":"10.1016/j.sbi.2026.103226","url":null,"abstract":"<div><div>Membrane lipids can bind to specific sites on membrane proteins in a ligand-like manner and modulate protein structure and function. Molecular dynamics simulations encompass a suite of approaches to identify, characterise, and explain the atomic-level mechanisms that underlie the functional effects of ligand-like lipids on membrane proteins. Simulations have shown good agreement with available structural data on lipid-protein interactions. Building on successes, simulations are now used to identify new interactions and mechanisms <em>de novo</em> for a given membrane protein. In this age of abundance, it is increasingly possible to analyse patterns across large groups of proteins and in ever more complex membrane environments. The dawn of machine learning approaches in lipid-protein cofolding holds considerable promise to synergistically capitalise on this availability of simulation data and uncover new facets of ligand-like lipid biology.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"97 ","pages":"Article 103226"},"PeriodicalIF":6.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146257423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-04-01Epub Date: 2026-02-12DOI: 10.1016/j.sbi.2026.103219
Brian M. Farrell , Markus A. Seeliger
Drug-target residence time is a crucial determinant of pharmacological efficacy, complementing traditional equilibrium affinity measures. Variations in residence time influence drug selectivity, therapeutic windows, and resistance development, yet its molecular underpinnings remain incompletely understood. Here we review factors governing residence time, including kinetic parameters and structural influences, and examine how mutations can alter dissociation rates to confer drug resistance. We highlight recent advances in experimental and computational methods, such as molecular dynamics simulations, that enable prediction and rational design of compounds with optimized residence times. These insights underscore the importance of incorporating kinetic considerations into drug discovery to improve efficacy and overcome resistance. Our findings suggest that optimizing residence time offers a promising strategy to enhance therapeutic outcomes for diverse diseases.
{"title":"Altered residence time as a cause of drug resistance","authors":"Brian M. Farrell , Markus A. Seeliger","doi":"10.1016/j.sbi.2026.103219","DOIUrl":"10.1016/j.sbi.2026.103219","url":null,"abstract":"<div><div>Drug-target residence time is a crucial determinant of pharmacological efficacy, complementing traditional equilibrium affinity measures. Variations in residence time influence drug selectivity, therapeutic windows, and resistance development, yet its molecular underpinnings remain incompletely understood. Here we review factors governing residence time, including kinetic parameters and structural influences, and examine how mutations can alter dissociation rates to confer drug resistance. We highlight recent advances in experimental and computational methods, such as molecular dynamics simulations, that enable prediction and rational design of compounds with optimized residence times. These insights underscore the importance of incorporating kinetic considerations into drug discovery to improve efficacy and overcome resistance. Our findings suggest that optimizing residence time offers a promising strategy to enhance therapeutic outcomes for diverse diseases.</div></div>","PeriodicalId":10887,"journal":{"name":"Current opinion in structural biology","volume":"97 ","pages":"Article 103219"},"PeriodicalIF":6.1,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146171551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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